From Niemann H. et al., “Knowledge Based Image Understanding by Iterative Optimization,” in KI-96, vol. 1137, pages 287-301, Springer-Verlag Berlin, 1996, it is known to use a semantic network for image understanding, where image objects as well as their symbolic relationships, attributes, etc. are formed using an initial segmentation process. The segmentation process is performed with the aid of knowledge that is independent of a task, with merely knowledge generally valid for all, or nearly all, types of images being utilized, such knowledge relating to colors, texture, or shape, for instance.
The image objects thus generated constitute an initial description of the image. This symbolically existing initial description constitutes an interface with regard to knowledge-based processing. Starting out from the initial description of the image, optimization processing is performed to eventually generate a semantic network enabling an optimal representation of the knowledge contained in the image.
These optimization processes are carried out by using knowledge required for a respective specific task, and its internal contexture. As the final result of optimization processing, a semantic network constituting an image interpretation is obtained.
One typical difficulty of a like object-based image processing method resides in the extraction of such image objects that are excellent reproductions of meaningful objects in the existing image material in accordance with a set task. In accordance with the above description, for an extraction of image objects one uses segmenting processes that are free of preliminary knowledge and carry out an extraction of image objects on the basis of a homogeneity criterion that relates to relatively general parameters such as color, texture, or shape.
Due to the heterogeneity of objects to be meaningfully described in image materials, due to the presence of noise, due to locally limited blanketing or due to the limited information that can be made available for a specific image area, such object-based image processing methods exhibit considerable drawbacks in that much more voluminous information would very often be necessary for. being able to make decisions with regard to the formation of image objects. This restricts the flexibility and applicability of such object-based image processing methods.
This is true nor only for the field of image processing, but also for many tasks where particular information is to be obtained from an arbitrary data structure comprised of single data, for it is a necessary step to meaningfully group single data contained in the data structure into superordinate units, i.e., structure objects, in accordance with a respective task.
It accordingly is an object of the present invention to furnish a computer-implemented method for pattern recognition and object-oriented processing of data structures that is capable of carrying out high-quality grouping of single data contained in a data structure in accordance with the requirements of a respective task.